Question: “How can I crop raster objects to vector objects, and extract the summary of raster pixels?”

Objectives:

  • Crop a raster to the extent of a vector layer.
  • Extract values from a raster that correspond to a vector file overlay.

Keypoints:

  • Use the crop() function to crop a raster object.
  • Use the extract() function to extract pixels from a raster object that fall within a particular extent boundary.
  • Use the extent() function to define an extent.
library(dplyr)
library(sf)
library(tibble)
library(ggplot2)
library(raster)
library(rgdal)
library(ggplot2)
library(dplyr)
library(here)

Introduction

copied from the carpentry lesson Manipulating Raster Data).

We often work with spatial layers that have different spatial extents. The spatial extent of a shapefile or R spatial object represents the geographic “edge” or location that is the furthest north, south east and west. Thus is represents the overall geographic coverage of the spatial object.

Image Source: National Ecological Observatory Network (NEON) The graphic below illustrates the extent of several of the spatial layers that we have worked with in this workshop:

Image Source: DCC

Frequent use cases of cropping a raster file include reducing file size and creating maps. Sometimes we have a raster file that is much larger than our study area or area of interest. It is often more efficient to crop the raster to the extent of our study area to reduce file sizes as we process our data. Cropping a raster can also be useful when creating pretty maps so that the raster layer matches the extent of the desired vector layers.

Import the raster

Data available here.

DSM_TUD <- raster(here("data","tud-dsm.tif"))
DTM_TUD <- raster(here("data","tud-dtm.tif"))
CHM_TUD <- DSM_TUD - DTM_TUD

CHM_TUD_df <- as.data.frame(CHM_TUD, xy = TRUE)

oai_boundary_tudlib <- st_as_sfc(st_bbox(raster(here("data","tudlib-rgb.tif"))))

Crop a Raster Using Vector Extent

We can use the crop() function to crop a raster to the extent of another spatial object. To do this, we need to specify the raster to be cropped and the spatial object that will be used to crop the raster. R will use the extent of the spatial object as the cropping boundary.

To illustrate this, we will crop the Canopy Height Model (CHM) to only include the area of interest (AOI). Let’s start by plotting the full extent of the CHM data and overlay where the AOI falls within it. The boundaries of the AOI will be colored blue, and we use fill = NA to make the area transparent.

ggplot() +
  geom_raster(data = CHM_TUD_df, aes(x = x, y = y, fill = layer)) + 
  scale_fill_gradientn(name = "Canopy Height", colors = terrain.colors(10)) +
  geom_sf(data = oai_boundary_tudlib, color = "blue", fill = NA) +
  coord_sf()

Now that we have visualized the area of the CHM we want to subset, we can perform the cropping operation. We are going to use the crop() function from the raster package to create a new object with only the portion of the CHM data that falls within the boundaries of the AOI.

CHM_TUD_Cropped <- crop(x = CHM_TUD, y = st_as_sf(oai_boundary_tudlib))

Now we can plot the cropped CHM data, along with a boundary box showing the full CHM extent. However, remember, since this is raster data, we need to convert to a data frame in order to plot using ggplot. To get the boundary box from CHM, the st_bbox() will extract the 4 corners of the rectangle that encompass all the features contained in this object. The st_as_sfc() converts these 4 coordinates into a polygon that we can plot:

CHM_TUD_Cropped_df <- as.data.frame(CHM_TUD_Cropped, xy = TRUE)

ggplot() +
  geom_sf(data = st_as_sfc(st_bbox(CHM_TUD)), fill = "green",
          color = "green", alpha = .2) +  
  geom_raster(data = CHM_TUD_Cropped_df,
              aes(x = x, y = y, fill = layer)) + 
  scale_fill_gradientn(name = "Canopy Height", colors = terrain.colors(10)) + 
  coord_sf()

The plot above shows that the full CHM extent (plotted in green) is much larger than the resulting cropped raster. Our new cropped CHM now has the same extent as the aoi_boundary_HARV object that was used as a crop extent (blue border below).

ggplot() +
  geom_raster(data = CHM_TUD_Cropped_df,
              aes(x = x, y = y, fill = layer)) + 
  geom_sf(data = oai_boundary_tudlib, color = "blue", fill = NA) + 
  scale_fill_gradientn(name = "Canopy Height", colors = terrain.colors(10)) + 
  coord_sf()

We can look at the extent of all of our other objects for this field site.

st_bbox(CHM_TUD)
    xmin     ymin     xmax     ymax 
 83569.5 445251.5  87180.0 447180.0 
st_bbox(CHM_TUD_Cropped)
    xmin     ymin     xmax     ymax 
 85272.0 446295.0  85661.5 446694.0 
st_bbox(oai_boundary_tudlib)
     xmin      ymin      xmax      ymax 
 85272.00 446295.20  85661.28 446694.24 
leisure_locations_selection <- st_read(here("data", "delft-leisure.shp")) %>% 
  filter(leisure %in% c("playground", "picnic_table"))
Reading layer `delft-leisure' from data source 
  `/Users/ccottineau/Documents/GitHub/geospatial-data-carpentry-tud-2022-11/data/delft-leisure.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 298 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 81863.21 ymin: 442621.1 xmax: 87370.15 ymax: 449345.1
Projected CRS: Amersfoort / RD New
st_bbox(leisure_locations_selection)
     xmin      ymin      xmax      ymax 
 81863.21 442792.82  86719.87 449007.92

Our plot location extent is not the largest but is larger than the AOI Boundary. It would be nice to see our vegetation plot locations plotted on top of the Canopy Height Model information.

Challenge :

Solution

CHM_plots_TUDcrop <- crop(x = CHM_TUD, y = leisure_locations_selection)

CHM_plots_TUDcrop_df <- as.data.frame(CHM_plots_TUDcrop, xy = TRUE)

ggplot() + 
  geom_raster(data = CHM_plots_TUDcrop_df, aes(x = x, y = y, fill = layer)) + 
  scale_fill_gradientn(name = "Canopy Height", colors = terrain.colors(10)) + 
  geom_sf(data = leisure_locations_selection) + 
  coord_sf()

In the plot above, created in the challenge, all the vegetation plot locations (black dots) appear on the Canopy Height Model raster layer except for one. One is situated on the blank space to the left of the map. Why?

A modification of the first figure in this episode is below, showing the relative extents of all the spatial objects. Notice that the extent for our vegetation plot layer (black) extends further west than the extent of our CHM raster (bright green). The crop() function will make a raster extent smaller, it will not expand the extent in areas where there are no data. Thus, the extent of our vegetation plot layer will still extend further west than the extent of our (cropped) raster data (dark green).

Image Source: DCC # Define an extent

So far, we have used a shapefile to crop the extent of a raster dataset. Alternatively, we can also the extent() function to define an extent to be used as a cropping boundary. This creates a new object of class extent. Here we will provide the extent() function our xmin, xmax, ymin, and ymax (in that order).

# extent(CHM_TUD_Cropped_df)
new_extent <- extent(85272.25, 85661.25, 446295.2, 446693.8)
class(new_extent)
[1] "Extent"
attr(,"package")
[1] "raster"

TIP: The extent can be created from a numeric vector (as shown above), a matrix, or a list. For more details see the extent() function help file (?raster::extent).

Once we have defined our new extent, we can use the crop() function to crop our raster to this extent object.

CHM_TUD_manual_cropped <- crop(x = CHM_TUD, y = new_extent)

To plot this data using ggplot() we need to convert it to a dataframe.

CHM_TUD_manual_cropped_df <- as.data.frame(CHM_TUD_manual_cropped, xy = TRUE)

Now we can plot this cropped data. We will show the AOI boundary on the same plot for scale.

ggplot() + 
  geom_sf(data = oai_boundary_tudlib, color = "blue", fill = NA) +
  geom_raster(data = CHM_TUD_manual_cropped_df,
              aes(x = x, y = y, fill = layer)) + 
  scale_fill_gradientn(name = "Canopy Height", colors = terrain.colors(10)) + 
  coord_sf()

Extract Raster Pixels Values Using Vector Polygons

Often we want to extract values from a raster layer for particular locations - for example, plot locations that we are sampling on the ground. We can extract all pixel values within 20m of our x,y point of interest. These can then be summarized into some value of interest (e.g. mean, maximum, total).

Image Source: National Ecological Observatory Network (NEON) To do this in R, we use the extract() function. The extract() function requires:

The raster that we wish to extract values from, The vector layer containing the polygons that we wish to use as a boundary or boundaries, we can tell it to store the output values in a data frame using df = TRUE. (This is optional, the default is to return a list, NOT a data frame.) . We will begin by extracting all canopy height pixel values located within our aoi_boundary_HARV polygon which surrounds the tower located at the NEON Harvard Forest field site.

tree_height <- extract(x = CHM_TUD, y = st_as_sf(oai_boundary_tudlib), df = TRUE)

str(tree_height)
'data.frame':   621642 obs. of  2 variables:
 $ ID   : num  1 1 1 1 1 1 1 1 1 1 ...
 $ layer: num  5.57 5.22 5.18 4.77 2.88 ...

When we use the extract() function, R extracts the value for each pixel located within the boundary of the polygon being used to perform the extraction - in this case the aoi_boundary_HARV object (a single polygon). Here, the function extracted values from 621,642 pixels.

We can create a histogram of tree height values within the boundary to better understand the structure or height distribution of trees at our site. We will use the column layer from our data frame as our x values, as this column represents the tree heights for each pixel.

ggplot() + 
  geom_histogram(data = tree_height, aes(x = layer)) +
  ggtitle("Histogram of CHM Height Values (m)") +
  xlab("Tree Height") + 
  ylab("Frequency of Pixels")

We can also use the summary() function to view descriptive statistics including min, max, and mean height values. These values help us better understand vegetation at our field site.

summary(tree_height$layer)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -3.460   0.000   0.375   4.343   8.523  36.729

Summarize Extracted Raster Values

We often want to extract summary values from a raster. We can tell R the type of summary statistic we are interested in using the fun = argument. Let’s extract a mean height value for our AOI. Because we are extracting only a single number, we will not use the df = TRUE argument.

mean_tree_height_AOI <- extract(x = CHM_TUD, y = st_as_sf(oai_boundary_tudlib), fun = mean)

head(mean_tree_height_AOI)
         [,1]
[1,] 4.342554

It appears that the mean height value, extracted from our LiDAR data derived canopy height model is 4.3 meters.

Extract Data using x,y Locations

We can also extract pixel values from a raster by defining a buffer or area surrounding individual point locations using the extract() function. To do this we define the summary argument (fun = mean) and the buffer distance (buffer = 20) which represents the radius of a circular region around each point. By default, the units of the buffer are the same units as the data’s CRS. All pixels that are touched by the buffer region are included in the extract.

Image Source:National Ecological Observatory Network (NEON)

Let’s put this into practice by figuring out the mean tree height in the 20m around the tower location (point_HARV). Because we are extracting only a single number, we will not use the df = TRUE argument.

point_Delft <- st_read(here("data", "delft-leisure.shp"))
Reading layer `delft-leisure' from data source 
  `/Users/ccottineau/Documents/GitHub/geospatial-data-carpentry-tud-2022-11/data/delft-leisure.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 298 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 81863.21 ymin: 442621.1 xmax: 87370.15 ymax: 449345.1
Projected CRS: Amersfoort / RD New
mean_tree_height_tower <- extract(x = CHM_TUD,
                                  y = point_Delft,
                                  buffer = 20,
                                  fun = mean)

mean_tree_height_tower
  [1]          NA  0.18382418          NA          NA          NA          NA
  [7]  0.14741693          NA          NA  0.95534103          NA          NA
 [13]          NA          NA          NA          NA          NA          NA
 [19]          NA  1.08576819  1.77810457  1.50813700          NA          NA
 [25]          NA          NA          NA          NA  3.41709123          NA
 [31]          NA  5.44526759          NA          NA          NA          NA
 [37]          NA          NA          NA          NA          NA          NA
 [43]          NA          NA          NA          NA  1.26241786          NA
 [49]          NA          NA          NA          NA          NA  3.63058446
 [55]          NA          NA          NA  2.64284311          NA          NA
 [61]          NA          NA  2.18177335          NA          NA          NA
 [67]          NA          NA          NA          NA  1.71895883          NA
 [73]  4.59002702          NA  9.22261513  3.71037319  3.67939371  2.67243673
 [79]  4.01660438          NA          NA          NA          NA          NA
 [85]          NA          NA          NA  5.09733638          NA          NA
 [91]  3.60859259          NA          NA          NA  1.93923010  5.21877630
 [97]          NA          NA          NA          NA          NA          NA
[103]          NA          NA          NA  1.77163454          NA          NA
[109]          NA          NA  1.70136037  2.58267291  4.21806988          NA
[115]          NA          NA          NA          NA          NA          NA
[121]          NA          NA 10.30380493  2.28110616          NA          NA
[127]          NA          NA          NA  3.64850905          NA  0.08129702
[133]          NA  2.06486905 11.40954040          NA          NA          NA
[139]  2.08243861  1.11961589  6.40306065  6.41666083  6.71693856  5.15843022
[145]  4.27773571          NA          NA          NA          NA          NA
[151]          NA          NA          NA          NA          NA          NA
[157]          NA  3.32952196          NA          NA          NA          NA
[163]          NA          NA          NA          NA          NA          NA
[169]          NA          NA          NA          NA  2.07421889          NA
[175]  1.94442299  2.60783294          NA          NA          NA          NA
[181]          NA  7.78255215          NA          NA          NA  1.21956164
[187]  2.31018698          NA          NA          NA          NA  7.29981885
[193]  2.72441063          NA          NA          NA  0.04019622  6.68842712
[199]  6.17018350  1.59578616  0.66848060  5.40900358          NA          NA
[205]          NA  2.25253693          NA          NA  3.20768940          NA
[211]          NA  0.25329162          NA          NA          NA  1.00615888
[217]  3.88431955          NA 10.92242381          NA          NA          NA
[223]          NA          NA  1.77282882          NA          NA          NA
[229]          NA          NA          NA          NA  1.34524428  1.75811156
[235]  1.87248210  1.37764249  1.50010280  2.11123471  2.40555998  1.10978271
[241]  0.85010793  2.65174185  2.63599304  2.40464816          NA          NA
[247]          NA          NA          NA 10.16022778          NA          NA
[253]          NA          NA          NA          NA          NA          NA
[259]          NA          NA  0.61521977          NA  2.39372841          NA
[265]          NA          NA          NA          NA          NA          NA
[271]          NA          NA          NA          NA          NA          NA
[277]          NA          NA  5.96494518  2.29402613          NA          NA
[283]  2.52794784  0.73166368          NA  1.58280219  4.89034569  0.07626293
[289]  0.40960883  3.79256709          NA          NA          NA  3.07805207
[295]          NA          NA          NA          NA

Challenge: Extract Raster Height Values For Plot Locations

Solution

leisure_locations_selection <- st_read(here("data", "delft-leisure.shp")) %>% 
  filter(leisure %in% c("playground", "picnic_table"))
Reading layer `delft-leisure' from data source 
  `/Users/ccottineau/Documents/GitHub/geospatial-data-carpentry-tud-2022-11/data/delft-leisure.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 298 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 81863.21 ymin: 442621.1 xmax: 87370.15 ymax: 449345.1
Projected CRS: Amersfoort / RD New
# extract data at each plot location
mean_tree_height_plots_TUD <- extract(x = CHM_TUD,
                                       y = leisure_locations_selection,
                                       buffer = 20,
                                       fun = mean,
                                       df = TRUE)

# view data
head(mean_tree_height_plots_TUD)
  ID    layer
1  1       NA
2  2 0.955341
3  3       NA
4  4       NA
5  5       NA
6  6       NA
# plot data
ggplot(data = mean_tree_height_plots_TUD, aes(ID, layer)) + 
  geom_col() + 
  ggtitle("Mean Tree Height at each Plot") + 
  xlab("Plot ID") + 
  ylab("Tree Height (m)")

Summary and keypoints.

We have seen how to crop a raster to the extent of a vector layer and how to extract values from a raster that correspond to a vector file overlay.

In short: